from scipy.stats import multivariate_normal
from tf_util.families import family_from_str
from efn_util import model_opt_hps
import os, sys
os.chdir('../')
exp_fam = str(sys.argv[1])
D = int(sys.argv[2])
give_inverse_hint = int(sys.argv[3]) == 1
random_seed = int(sys.argv[4])
dir_str = str(sys.argv[5])
profile = True
TIF_flow_type, nlayers, scale_layer, lr_order = model_opt_hps(exp_fam, D)
nlayers = 30
lr_order = -3
flow_dict = {'latent_dynamics':None, \
'scale_layer':False, \
'TIF_flow_type':TIF_flow_type, \
'repeats':nlayers}
fam_class = family_from_str(exp_fam)
family = fam_class(D)
family.load_data()
family.select_train_test_sets(500)
param_net_input_type = 'eta'
import numpy as np
from scipy.stats import multivariate_normal
from families import family_from_str
from efn_util import model_opt_hps
import os, sys
os.chdir('../')
exp_fam = str(sys.argv[1])
D = int(sys.argv[2])
nlayers = int(sys.argv[3])
give_inverse_hint = int(sys.argv[4]) == 1
random_seed = int(sys.argv[5])
dir_str = str(sys.argv[6])
TIF_flow_type, _, lr_order = model_opt_hps(exp_fam, D)
flow_dict = {'latent_dynamics':None, \
'TIF_flow_type':TIF_flow_type, \
'repeats':nlayers}
fam_class = family_from_str(exp_fam)
family = fam_class(D)
param_net_input_type = 'eta'
cost_type = 'KL'
K_eta = 100
M_eta = 1000
stochastic_eta = True
dist_seed = 0
max_iters = 1000000